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Mohammad Ali Moni, Pietro Lio’, Genetic Profiling and Comorbidities of Zika Infection, The Journal of Infectious Diseases, Volume 216, Issue 6, 15 September 2017, Pages 703–712, https://doi.org/10.1093/infdis/jix327
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Abstract
The difficulty in distinguishing infection by Zika virus (ZIKV) from other flaviviruses is a global health concern, particularly given the high risk of neurologic complications (including Guillain-Barré syndrome [GBS]) with ZIKV infection.
We developed quantitative frameworks to compare and explore infectome, diseasome, and comorbidity of ZIKV infections. We analyzed gene expression microarray and RNA-Seq data from ZIKV, West Nile fever (WNF), chikungunya, dengue, yellow fever, Japanese encephalitis virus, GBS, and control datasets. Using neighborhood-based benchmarking and multilayer network topology, we constructed relationship networks based on the Online Mendelian Inheritance in Man database and our identified significant genes.
ZIKV infections showed dysregulation in expression of 929 genes. Forty-seven genes were highly expressed in both ZIKV and dengue infections. However, ZIKV shared <15 significant transcripts with other flavivirus infections. Notably, dysregulation of MAFB and SELENBP1 was common to ZIKV, dengue, and GBS infection; ATF5, TNFAIP3, and BAMB1 were common to ZIKV, dengue, and WNF; and NAMPT and PMAlP1 were common to ZIKV, GBS, and WNF. Phylogenetic, ontologic, and pathway analyses showed that ZIKV infection most resembles dengue fever.
We have developed methodologies to investigate disease mechanisms and predictions for infectome, diseasome, and comorbidities quantitatively, and identified particular similarities between ZIKV and dengue infections.
Comorbidity, the co-occurrence of ≥2 diseases simultaneously in the same patient, can be attributed to the disease and infection connections at the molecular level [1]. From a genetic perspective, a pair of diseases and/or infections are connected when they are both associated with some of the same dysregulated genes [2]. The analysis of gene–disease/infection interaction allows us to identify a set of novel interactions that could further explain molecular mechanism associated with viral infections (infectome) and noninfectious diseases (diseasome) [3]. Infections (acute and chronic) are often associated with comorbidities that increase the risk of further morbidity and mortality [4].
The Zika virus (ZIKV) is a mosquito-borne RNA arbovirus of the Flaviviridae family, which also includes dengue, Japanese encephalitis virus (JEV), chikungunya, West Nile fever (WNF), and yellow fever viruses [5]. ZIKV is transmitted by female Aedes mosquitoes, especially Aedes aegypti and Aedes albopictus, which also carry dengue and chikungunya viruses [6]. Similar to the transmission of dengue and chikungunya viruses, the main transmission cycle of ZIKV occurs between urban Aedes mosquitoes and humans [7]. Dengue and chikungunya are genetically distant relatives but ZIKV is closely related to dengue, sufficient to elicit a false-positive dengue test [8, 9]. ZIKV is also related to the yellow fever, JEV, and WNF viruses [10]. While infected individuals can often be asymptomatic or show mild symptoms, there is mounting concern about reports linking ZIKV infection to fetal and newborn microcephaly and serious neurological complications, such as Guillain-Barré syndrome (GBS) [10]. Other arboviral diseases such as WNF, JEV, chikungunya, and dengue had already been reported to cause GBS [11].
Zika has had a low clinical impact to date but its true potential is unfolding with increasing detection of congenital malformations, GBS, and other neurological and autoimmune syndromes in patients with recent history of ZIKV infection [12]. These infections cause similar clinical presentation with prominent fever, headache, rash, myalgias (muscle aches), and arthralgias. However, as ZIKV infection also causes rash similar to measles or dengue, these diseases need to be ruled out before suspecting Zika. However, the clinical presentations and complications of Zika are not well characterized. Thus, a diagnosis of Zika is primarily based on the presenting symptoms, history of travel, and exclusion of diseases with similar symptoms (ie, measles, rubella, and dengue) [12]. Therefore, a high index of suspicion by an astute physician is necessary for diagnosis of Zika fever, especially considering the similar symptoms of dengue and chikungunya and the similar regions where these 2 diseases are already endemic [10].
A causal link and the mechanisms between the ZIKV and Zika-associated disorders are still to be proven and biomarkers are urgently needed to investigate these associations [13]. To understand the overall mechanism, we examined the infectome, diseasome, and comorbidity associations of ZIKV infections. We also undertook a comprehensive phylogenetic and pathway study to establish the genetic relationship among these viruses and to compare the classification based on molecular phylogeny. To our knowledge, this is the first time a highly heterogenous infectome, diseasome, and comorbidity network centered around ZIKV infections using disease–gene associations has been characterized.
MATERIALS AND METHODS
Data
To investigate the impact of ZIKV infection at the molecular level, we employed global transcriptome analyses (RNA-seq) and gene expression microarray datasets. Data used in this study, including complete genome of the viruses, were obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/). We studied 7 different datasets for our analysis with accession numbers GSE78711, GSE51808, GSE69980, GSE57330, GSE46681, GSE13699, and GSE31014 [10, 14–18]. The ZIKV dataset (GSE78711) is an in vitro system with hiPSCs (Human Induced Pluripotent Stem Cells) and hNPC (Human Nucleus Pulposus Cells) immature neurones using RNAseq. The dengue dataset (GSE51808) is an Affymetrix HT HG-U133+ PM array plate data from whole-blood samples of 28 dengue patients hospitalized at the Siriraj Hospital in Bangkok, Thailand. The chikungunya microarray dataset (GSE69980) is from RNA interference screen experiments of human whole-genome catalogue arrays. The JEV dataset (GSE57330) is an Affymetrix human gene expression array of uninfected and infected samples at each time point. The WNF dataset (GSE46681) is a differential gene expression by human peripheral blood mononuclear cells and macrophages from asymptomatic and severe patients with WNV infection. The yellow fever dataset (GSE13699) is a yellow fever vaccine (YF17D) tested on 2 populations in Canada and Switzerland and an in vitro system using Illumina Beadchip technology. The GBS dataset (GSE31014) is an Affymetrix microarray data from peripheral blood leukocytes of patients with GBS.
The gene–disease association data used in this study were collected from the Online Mendelian Inheritance in Man (OMIM) database (http://www.ncbi.nlm.nih.gov/omim/), a curated database of all known disease genes, their associated disorders, and genotype–phenotype relationships [19]. We classified each disorder into 21 primary disorder classes based on the physiological system affected as introduced in Goh et al [20]. Disorders having distinct multiple clinical features are assigned to the “multiple” class.
Methods
The method of global gene expression analysis using RNA-Seq and oligonucleotide microarrays has proven to be a sensitive method to develop and refine the molecular determinants of human disorders. Using these technologies and global transcriptome analyses, we compared the gene expression profiles of Zika, WNF, chikungunya, dengue, yellow fever, and JEV infections as well as GBS. To avoid the problems of comparing mRNA expression data of different platforms and experimental systems, we normalized and calibrated the gene expression data in each sample (disease state or control) using the Z-score transformation () for each disease–gene expression matrix using , where SD is the standard devi ation and represents the expression value of gene in sample . This transformation allows for the direct comparison of gene expression values across various samples and diseases. We applied linear regression approach on the time series data to obtain a combined t test statistic between 2 conditions. Data were log2 transformed and we calculated expression level for each gene using a linear regression model: , where is the gene expression value and is a disease state (disease or control). The coefficients and are the parameters of this model and were estimated by least squares.
Student unpaired t test was performed to identify genes that were differentially expressed in patients over normal samples, and significant genes were selected. A threshold of at least log2 fold change and a P value for the t tests of <3 × 10–4 were chosen. In addition, a 2-way analysis of variance with Bonferroni post hoc test was used to establish statistical significance between groups (P < 0.01).
The co-occurrence refers to the number of shared genes in the GDN. The common neighbor is the based on the Jaccard coefficient method, where the edge prediction score for the node pair is as:
where is the set of all edges. We have used our developed R software packages “comoR” [21] and “POGO” [22] to compute novel estimators of the disease comorbidity associations.
To establish a comprehensive phylogeny of the flavivirus, we obtained the complete genomic sequence from the NCBI database (http://www.ncbi.nlm.nih.gov/genome/viruses/variation/). The multiple sequence alignment program ClustalW [23] was used to obtain an optimal nucleotide sequence alignment file. Phylograms for the entire sequence were obtained by MEGA [24] based on aligned nucleotide sequences. We also determined genetic distance by the proportional distance method. A proportional distance matrix was transformed to calculate the pairwise nucleotide sequence identity between all virus pairs. For tree building, various genetic distance matrices were used for the neighbor-joining method, which calculated bootstrap confidence intervals of 500 heuristic search replicates and confidence probability of the genetic distance by a standard error test.
To get further insight into the molecular pathways of Zika infection, we have performed pathway and gene ontology analysis using the David bioinformatics resources (https://david-d.ncifcrf.gov/) and KEGG pathways database [25].
RESULTS
Infectome and Diseasome Analysis
To investigate the host response to ZIKV, we analyzed the gene expression patterns of Zika-infected patient tissues, comparing with normal subjects using global transcriptome analyses (RNA-Seq) (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78711) [10]. We found that 929 genes (P < 3 × 10–4, >1 log2 fold change) were differentially expressed relative to healthy controls in which 341 genes were significantly up-regulated and 588 genes were significantly down-regulated (Supplementary Table 1).
To observe the association of ZIKV infections with other 5 infections and GBS disease, we have collected messenger RNA (mRNA) microarray raw data associated with each infection or disease. After several steps of statistical analysis, we have selected the most significant over- and underexpressed genes for each infection and disease. Our analyses identified a large number of differentially expressed genes (976 in dengue, 60 in yellow fever, 293 in JEV, 526 in chikungunya, 288 in WNF, and 381 in GBS) in different viral infections and GBS (Supplementary Tables 2–7).
We also performed cross-comparative analysis to find the common significant genes between each disease and ZIKV infection. We observed that ZIKV infection shares 47, 12, 15, 15, 14, and 10 significant genes with dengue, chikungunya, WNF, yellow fever, JEV, and GBS respectively. To find statistically significant associations among these infections and diseases, we built an infectome–diseasome relationships network centered on the ZIKV infection in which 2 diseases are comorbid if there exists 1 or more genes that are associated with both diseases (Figure 1 and Supplementary Table 8). Notably, 2 significant genes, MAFB and SELENBP1, are commonly dysregulated among ZIKV, dengue, and GBS; 3 significant genes, ATF5, TNFAIP3, and BAMB1, are commonly dysregulated among ZIKV, dengue, and WNF; and 2 significant genes, NAMPT and PMAlP1, are commonly dysregulated among ZIKV, GBS, and WNF. Interestingly, only 2 genes (BAMBI and TNAFIP3) are common among ZIKV, WNF, JEV, and dengue infections. However, 4 genes (IFT2, IFT3, DDX58, and HERC5) play an important role and are differentially expressed among ZIKV, WNF, JEV, and yellow fever infections. Furthermore, we have studied the discordant analysis of dysregulated genes in ZIKV infection with other 6 infections and disease as shown in Figure 2. It is notable that the number of discordant genes is high between ZIKV and dengue infection, whereas only 2 genes are found between ZIKV and yellow fever infection (Figure 2 and Supplementary Table 8). These discordant genes could be used to differentiate ZIKV from other infections.
Phylogenetic Analysis
We observed the phylogenetic association among ZIKV, WNF, chikungunya, dengue, yellow fever, and JEV using their nucleotide sequences as shown in Figure 3. This tree was constructed by the neighbor-joining method of MEGA [26]. Each number at nodes is the percentage of 500 bootstrap replicate support. This neighbor-joining tree based on a proportional distance of nucleotide sequence is shown in Figure 3. Based on the gene expression and phylogenetic analysis of the viral genome sequence, we found that ZIKV and dengue infections have a strong association between them.
Comorbidity Analysis
Considering the significantly dysregulated genes altered by ZIKV infection, and gene–disease association information, we have constructed GDNs, which are used to explore the shared genetic associations and infectome disease comorbidity network (Figure 4). Starting from the bipartite graph, we generated biologically relevant network projections and constructed multirelational gene–disease network in which nodes are diseases or genes, and edges indicate association between gene and disease. This bipartite graph consists of 2 disjoint sets of nodes, where 1 set corresponds to all known genetic disorders and the other set corresponds to all of our identified significant genes for ZIKV infections. The list of disorders, disease genes, and associations between them were obtained from the OMIM, a compendium of human disease genes and phenotypes. We classified each disorder into 1 of 21 disorder categories based on the physiological system affected as introduced in [20].
In the GDN, nodes represent disease class or genes, and 2 disorders are connected to each other if they share at least 1 gene in which mutations are associated with both disease groups (Figure 4). The number of interlinked genes between ZIKV infection and other diseases indicates that metabolic (17 genes), neurological (14 genes), and cancer (15) disease categories are strongly associated with the ZIKV infection due to the highest number of genes shared between them (Figure 4 and Supplementary Table 9). Few genes are also shared between >2 categories of diseases—that is, those disease groups are only associated through at least those genes. For an instance, the gene FLCN was shared among ZIKV infection, cancer, respiratory, and dermatological diseases.
Pathway and Functional Correlation Analysis
Comparing the expression of the most differentially regulated genes, we found that 341 genes were significantly up-regulated and 588 genes were significantly down-regulated (Supplementary Table 1) in ZIKV infection. Pathways of the differentially expressed genes were analyzed using the KEGG pathway database (http://www.genome.jp/kegg/pathway.html) and functional annotation analysis tool DAVID v 6.8 (http://niaid.abcc.ncifcrf.gov) to identify overrepresented pathway groups among differentially expressed genes and to group them into functional categories. We observed that 3 significant pathways including protein processing in endoplasmic reticulum and aminoacyl–transfer RNA (tRNA) biosynthesis signaling pathways are associated with the significantly up-regulated genes for ZIKV infection. We also observed that 5 significant pathways including cell cycle, DNA replication, and Fanconi anemia pathways are identified using DAVID; these are associated with the significantly down-regulated genes for ZIKV infections. Genes associated with these pathways, fold enrichment, and corresponding adjusted P values are presented in Table 1.
KEGG ID . | Pathway . | Genes in the Pathway . | Fold Enrichment . | Adjusted P Value . |
---|---|---|---|---|
Up-regulated genes: | ||||
hsa04141 | Protein processing in endoplasmic reticulum | HERPUD1, DERL2, SYVN1, SEC24A, RRBP1, DDIT3, HYOU1, XBP1, YOD1, NFE2L2, DNAJC3, SEC24D, EIF2AK3, UBE2E2, SEC23B, SSR3 | 5 | 2.23 × 10–04 |
hsa00970 | Aminoacyl-tRNA biosynthesis | IARS, TARS, CARS, YARS, SARS, AARS, GARS, MARS | 6 | 2.75 × 10–02 |
hsa04710 | Circadian rhythm | CRY2, NR1D1, PER2, PER1, BHLHE40, BHLHE41 | 9 | 1.88 × 10–02 |
Down-regulated genes: | ||||
hsa04110 | Cell cycle | CDC7, E2F1, E2F2, CDK1, E2F3, RBL1, PKMYT1, ESPL1, CDC20, MCM2, MCM3, MCM4, CDK2, MCM5, MCM6, CCNB1, CDC45, CCNB2, CDKN2C, PCNA, BUB1B, ORC6, ORC1 | 7 | 2.36 × 10–10 |
hsa03030 | DNA replication | POLA2, MCM2, RNASEH2A, MCM3, MCM4, MCM5, MCM6, POLD3, DNA2, RFC3, POLE2, POLD1, PCNA | 13 | 7.92 × 10–09 |
hsa05166 | HTLV-I infection | E2F1, EGR1, KAT2A, ADCY3, E2F2, E2F3, EGR2, ADCY6, CDC20, FZD2, MYBL2, POLD3, FOS, WNT7B, POLE2, CDKN2C, POLD1, PCNA, BUB1B, WNT8B, AKT2 | 3 | 1.16 × 10–03 |
hsa00240 | Pyrimidine metabolism | NME4, POLD3, NME5, TYMS, POLE2, POLD1, RRM2, RRM1, POLA2, ENTPD1, TK2, TK1 | 4 | 5.50 × 10–03 |
hsa03460 | Fanconi anemia pathway | FANCL, FANCD2, FANCI, BRIP1, RMI2, BRCA1, FANCB, RAD51 | 6 | 2.14 × 10–02 |
KEGG ID . | Pathway . | Genes in the Pathway . | Fold Enrichment . | Adjusted P Value . |
---|---|---|---|---|
Up-regulated genes: | ||||
hsa04141 | Protein processing in endoplasmic reticulum | HERPUD1, DERL2, SYVN1, SEC24A, RRBP1, DDIT3, HYOU1, XBP1, YOD1, NFE2L2, DNAJC3, SEC24D, EIF2AK3, UBE2E2, SEC23B, SSR3 | 5 | 2.23 × 10–04 |
hsa00970 | Aminoacyl-tRNA biosynthesis | IARS, TARS, CARS, YARS, SARS, AARS, GARS, MARS | 6 | 2.75 × 10–02 |
hsa04710 | Circadian rhythm | CRY2, NR1D1, PER2, PER1, BHLHE40, BHLHE41 | 9 | 1.88 × 10–02 |
Down-regulated genes: | ||||
hsa04110 | Cell cycle | CDC7, E2F1, E2F2, CDK1, E2F3, RBL1, PKMYT1, ESPL1, CDC20, MCM2, MCM3, MCM4, CDK2, MCM5, MCM6, CCNB1, CDC45, CCNB2, CDKN2C, PCNA, BUB1B, ORC6, ORC1 | 7 | 2.36 × 10–10 |
hsa03030 | DNA replication | POLA2, MCM2, RNASEH2A, MCM3, MCM4, MCM5, MCM6, POLD3, DNA2, RFC3, POLE2, POLD1, PCNA | 13 | 7.92 × 10–09 |
hsa05166 | HTLV-I infection | E2F1, EGR1, KAT2A, ADCY3, E2F2, E2F3, EGR2, ADCY6, CDC20, FZD2, MYBL2, POLD3, FOS, WNT7B, POLE2, CDKN2C, POLD1, PCNA, BUB1B, WNT8B, AKT2 | 3 | 1.16 × 10–03 |
hsa00240 | Pyrimidine metabolism | NME4, POLD3, NME5, TYMS, POLE2, POLD1, RRM2, RRM1, POLA2, ENTPD1, TK2, TK1 | 4 | 5.50 × 10–03 |
hsa03460 | Fanconi anemia pathway | FANCL, FANCD2, FANCI, BRIP1, RMI2, BRCA1, FANCB, RAD51 | 6 | 2.14 × 10–02 |
Significantly up-regulated genes for Zika virus infection resulted in an enrichment of 3 KEGG pathways using DAVID. Significantly down-regulated genes for Zika virus infection resulted in an enrichment of 5 KEGG pathways using DAVID. Genes associated with these pathways, fold enrichment, and corresponding adjusted P values are presented.
Abbreviations: HTLV-I, Human T-lymphotropic virus; tRNA, transfer RNA.
KEGG ID . | Pathway . | Genes in the Pathway . | Fold Enrichment . | Adjusted P Value . |
---|---|---|---|---|
Up-regulated genes: | ||||
hsa04141 | Protein processing in endoplasmic reticulum | HERPUD1, DERL2, SYVN1, SEC24A, RRBP1, DDIT3, HYOU1, XBP1, YOD1, NFE2L2, DNAJC3, SEC24D, EIF2AK3, UBE2E2, SEC23B, SSR3 | 5 | 2.23 × 10–04 |
hsa00970 | Aminoacyl-tRNA biosynthesis | IARS, TARS, CARS, YARS, SARS, AARS, GARS, MARS | 6 | 2.75 × 10–02 |
hsa04710 | Circadian rhythm | CRY2, NR1D1, PER2, PER1, BHLHE40, BHLHE41 | 9 | 1.88 × 10–02 |
Down-regulated genes: | ||||
hsa04110 | Cell cycle | CDC7, E2F1, E2F2, CDK1, E2F3, RBL1, PKMYT1, ESPL1, CDC20, MCM2, MCM3, MCM4, CDK2, MCM5, MCM6, CCNB1, CDC45, CCNB2, CDKN2C, PCNA, BUB1B, ORC6, ORC1 | 7 | 2.36 × 10–10 |
hsa03030 | DNA replication | POLA2, MCM2, RNASEH2A, MCM3, MCM4, MCM5, MCM6, POLD3, DNA2, RFC3, POLE2, POLD1, PCNA | 13 | 7.92 × 10–09 |
hsa05166 | HTLV-I infection | E2F1, EGR1, KAT2A, ADCY3, E2F2, E2F3, EGR2, ADCY6, CDC20, FZD2, MYBL2, POLD3, FOS, WNT7B, POLE2, CDKN2C, POLD1, PCNA, BUB1B, WNT8B, AKT2 | 3 | 1.16 × 10–03 |
hsa00240 | Pyrimidine metabolism | NME4, POLD3, NME5, TYMS, POLE2, POLD1, RRM2, RRM1, POLA2, ENTPD1, TK2, TK1 | 4 | 5.50 × 10–03 |
hsa03460 | Fanconi anemia pathway | FANCL, FANCD2, FANCI, BRIP1, RMI2, BRCA1, FANCB, RAD51 | 6 | 2.14 × 10–02 |
KEGG ID . | Pathway . | Genes in the Pathway . | Fold Enrichment . | Adjusted P Value . |
---|---|---|---|---|
Up-regulated genes: | ||||
hsa04141 | Protein processing in endoplasmic reticulum | HERPUD1, DERL2, SYVN1, SEC24A, RRBP1, DDIT3, HYOU1, XBP1, YOD1, NFE2L2, DNAJC3, SEC24D, EIF2AK3, UBE2E2, SEC23B, SSR3 | 5 | 2.23 × 10–04 |
hsa00970 | Aminoacyl-tRNA biosynthesis | IARS, TARS, CARS, YARS, SARS, AARS, GARS, MARS | 6 | 2.75 × 10–02 |
hsa04710 | Circadian rhythm | CRY2, NR1D1, PER2, PER1, BHLHE40, BHLHE41 | 9 | 1.88 × 10–02 |
Down-regulated genes: | ||||
hsa04110 | Cell cycle | CDC7, E2F1, E2F2, CDK1, E2F3, RBL1, PKMYT1, ESPL1, CDC20, MCM2, MCM3, MCM4, CDK2, MCM5, MCM6, CCNB1, CDC45, CCNB2, CDKN2C, PCNA, BUB1B, ORC6, ORC1 | 7 | 2.36 × 10–10 |
hsa03030 | DNA replication | POLA2, MCM2, RNASEH2A, MCM3, MCM4, MCM5, MCM6, POLD3, DNA2, RFC3, POLE2, POLD1, PCNA | 13 | 7.92 × 10–09 |
hsa05166 | HTLV-I infection | E2F1, EGR1, KAT2A, ADCY3, E2F2, E2F3, EGR2, ADCY6, CDC20, FZD2, MYBL2, POLD3, FOS, WNT7B, POLE2, CDKN2C, POLD1, PCNA, BUB1B, WNT8B, AKT2 | 3 | 1.16 × 10–03 |
hsa00240 | Pyrimidine metabolism | NME4, POLD3, NME5, TYMS, POLE2, POLD1, RRM2, RRM1, POLA2, ENTPD1, TK2, TK1 | 4 | 5.50 × 10–03 |
hsa03460 | Fanconi anemia pathway | FANCL, FANCD2, FANCI, BRIP1, RMI2, BRCA1, FANCB, RAD51 | 6 | 2.14 × 10–02 |
Significantly up-regulated genes for Zika virus infection resulted in an enrichment of 3 KEGG pathways using DAVID. Significantly down-regulated genes for Zika virus infection resulted in an enrichment of 5 KEGG pathways using DAVID. Genes associated with these pathways, fold enrichment, and corresponding adjusted P values are presented.
Abbreviations: HTLV-I, Human T-lymphotropic virus; tRNA, transfer RNA.
In addition, to get further insight of the significantly dysregulated genes of the ZIKV infection, we performed gene ontology enrichment analysis using DAVID to identify overrepresented ontological groups among differentially expressed genes and to group them into functional categories. We observed that 18 significant gene ontology groups are associated with the significantly up-regulated genes for ZIKV infections (Table 2) and 25 significant gene ontology groups are associated with the significantly down-regulated genes for ZIKV infections (Table 3). Genes associated with these ontologies, fold enrichment, and corresponding adjusted P values are presented in Tables 2 and 3.
KEGG ID . | Pathway . | Genes in the Pathway . | Fold Enrichment . | Adjusted P Value . |
---|---|---|---|---|
GO:0030968 | Endoplasmic reticulum unfolded protein response | DERL2, CTH, TBL2, SYVN1, STC2, XBP1, AARS, CREB3L2, CREB3L1, YOD1, NFE2L2, EIF2AK3 | 15 | 6.28 × 10–07 |
GO:0034976 | Response to endoplasmic reticulum stress | CREBRF, HYOU1, HERPUD1, CEBPB, XBP1, BBC3, CREB3L2, TRIB3, PDIA5, FAM129A, EIF2AK3, DDIT3 | 9 | 9.34 × 10–05 |
GO:0000122 | Negative regulation of transcription from RNA polymerase II promoter | CREBRF, JDP2, NOG, CBX4, TRIB3, FOXO1, NFKB2, NFXL1, FLCN, TSC22D3, CRY2, NR1D1, HEY1, XBP1, SQSTM1, PER2, PER1, BHLHE40, SKIL, BHLHE41, MYC, KLF5, ERF, BMP2, RREB1, SMAD7, KLF10, RELB, NR4A3, MXD1, DDIT3, FNIP1, ATF3, TRPS1, VEGFA, NFIA | 3 | 7.08 × 10–05 |
GO:0045944 | Positive regulation of transcription from RNA polymerase II promoter | AKNA, NAMPT, NOG, FOXO1, NR3C1, NFKB2, FLCN, HEY1, XBP1, SQSTM1, NFAT5, CREB3L2, PER1, CREB3L1, FOSL1, MYC, ETV5, SERTAD1, KLF5, PID1, BMP2, CEBPB, MAFB, SMAD7, RELB, NR4A1, KLF15, SIX4, NR4A3, PPARGC1A, DDIT3, DDX58, ATF5, ATF3, DBP, CSRNP1, TRPS1, NCK1, VEGFA, IRF1, NFE2L2, NFIA, ACVR1 | 2 | 8.84 × 10–05 |
GO:0036499 | PERK-mediated unfolded protein response | HERPUD1, ATF3, ASNS, NFE2L2, EIF2AK3, DDIT3 | 27 | 4.93 × 10–04 |
GO:0006366 | Transcription from RNA polymerase II promoter | AKNA, NR3C1, NFKB2, NFXL1, POLR2A, XBP1, CREB3L2, NFAT5, CREB3L1, ETV5, MYC, FOSL1, KLF5, ZBTB7B, CEBPB, RREB1, MAFB, SIX4, KLF15, ATF5, ATF3, DBP, TRPS1, CSRNP1, IRF1, NFE2L2, NFIA | 3 | 7.66 × 10–04 |
GO:0006865 | Amino acid transport | SLC1A4, SLC1A5, SLC7A3, SLC7A1, SLC3A2, SLC7A5, MYC, SLC7A11 | 13 | 6.83 × 10–04 |
GO:0006418 | Transfer RNA aminoacylation for protein translation | IARS, TARS, CARS, YARS, SARS, AARS, GARS, MARS | 11 | 1.53 × 10–03 |
GO:0032922 | Circadian regulation of gene expression | NAMPT, CRY2, NR1D1, RELB, PER2, PER1, BHLHE40, BHLHE41, PPARGC1A | 9 | 1.65 × 10–03 |
GO:0036498 | IRE1-mediated unfolded protein response | HYOU1, DNAJB9, TSPYL2, SYVN1, XBP1, PDIA5, SRPRB, DNAJC3, WIPI1 | 8 | 1.94 × 10–03 |
GO:0034599 | Cellular response to oxidative stress | DHRS2, PYCR1, XBP1, FOXO1, NFE2L2, RAD52, SESN2, PPARGC1A, ETV5 | 8 | 3.24 × 10–03 |
GO:0070059 | Intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress | CEBPB, XBP1, BBC3, CHAC1, TRIB3, PMAIP1, DDIT3 | 12 | 3.76 × 10–03 |
GO:1990440 | Positive regulation of transcription from RNA polymerase II promoter in response to endoplasmic reticulum stress | ATF3, CEBPB, XBP1, CREB3L1, DDIT3 | 23 | 6.52 × 10–03 |
GO:0007623 | Circadian rhythm | ATF5, NAMPT, CRY2, NR1D1, DBP, KLF10, PER2, PER1, PPARGC1A | 7 | 8.16 × 10–03 |
GO:0042149 | Cellular response to glucose starvation | TBL2, XBP1, ASNS, NFE2L2, PMAIP1, EIF2AK3 | 11 | 1.90 × 10–02 |
GO:0006355 | Regulation of transcription, DNA-templated | CREBRF, ZNF555, ZNF844, ZNF79, ING2, ZNF805, TAF1D, NR3C2, ZXDB, NR3C1, NFKB2, ZNF654, POLR2A, TSC22D3, TSPYL2, HEY1, ZNF709, SND1, ZNF442, BHLHE40, BHLHE41, INO80D, MYC, SAMD4A, ZBTB7B, ZNF563, BMP2, ZNF264, CEBPB, SNAPC1, RREB1, LRIF1, SMAD7, SPTY2D1, SIX4, PPARGC1A, ZBTB43, ABCG1, DDIT3, ATF5, IGSF1, CDKN2AIP, ZNF433, ZNF460, RBM39, SP8, NFIA, GOLGB1 | 2 | 1.83 × 10–02 |
GO:0042752 | Regulation of circadian rhythm | MAGEL2, CRY2, NR1D1, KLF10, PER2, PER1, PPARGC1A | 8 | 2.60 × 10–02 |
GO:0071456 | Cellular response to hypoxia | HYOU1, EIF4EBP1, TBL2, STC2, BBC3, VEGFA, NFE2L2, PMAIP1, PPARGC1A | 5 | 3.54 × 10–02 |
KEGG ID . | Pathway . | Genes in the Pathway . | Fold Enrichment . | Adjusted P Value . |
---|---|---|---|---|
GO:0030968 | Endoplasmic reticulum unfolded protein response | DERL2, CTH, TBL2, SYVN1, STC2, XBP1, AARS, CREB3L2, CREB3L1, YOD1, NFE2L2, EIF2AK3 | 15 | 6.28 × 10–07 |
GO:0034976 | Response to endoplasmic reticulum stress | CREBRF, HYOU1, HERPUD1, CEBPB, XBP1, BBC3, CREB3L2, TRIB3, PDIA5, FAM129A, EIF2AK3, DDIT3 | 9 | 9.34 × 10–05 |
GO:0000122 | Negative regulation of transcription from RNA polymerase II promoter | CREBRF, JDP2, NOG, CBX4, TRIB3, FOXO1, NFKB2, NFXL1, FLCN, TSC22D3, CRY2, NR1D1, HEY1, XBP1, SQSTM1, PER2, PER1, BHLHE40, SKIL, BHLHE41, MYC, KLF5, ERF, BMP2, RREB1, SMAD7, KLF10, RELB, NR4A3, MXD1, DDIT3, FNIP1, ATF3, TRPS1, VEGFA, NFIA | 3 | 7.08 × 10–05 |
GO:0045944 | Positive regulation of transcription from RNA polymerase II promoter | AKNA, NAMPT, NOG, FOXO1, NR3C1, NFKB2, FLCN, HEY1, XBP1, SQSTM1, NFAT5, CREB3L2, PER1, CREB3L1, FOSL1, MYC, ETV5, SERTAD1, KLF5, PID1, BMP2, CEBPB, MAFB, SMAD7, RELB, NR4A1, KLF15, SIX4, NR4A3, PPARGC1A, DDIT3, DDX58, ATF5, ATF3, DBP, CSRNP1, TRPS1, NCK1, VEGFA, IRF1, NFE2L2, NFIA, ACVR1 | 2 | 8.84 × 10–05 |
GO:0036499 | PERK-mediated unfolded protein response | HERPUD1, ATF3, ASNS, NFE2L2, EIF2AK3, DDIT3 | 27 | 4.93 × 10–04 |
GO:0006366 | Transcription from RNA polymerase II promoter | AKNA, NR3C1, NFKB2, NFXL1, POLR2A, XBP1, CREB3L2, NFAT5, CREB3L1, ETV5, MYC, FOSL1, KLF5, ZBTB7B, CEBPB, RREB1, MAFB, SIX4, KLF15, ATF5, ATF3, DBP, TRPS1, CSRNP1, IRF1, NFE2L2, NFIA | 3 | 7.66 × 10–04 |
GO:0006865 | Amino acid transport | SLC1A4, SLC1A5, SLC7A3, SLC7A1, SLC3A2, SLC7A5, MYC, SLC7A11 | 13 | 6.83 × 10–04 |
GO:0006418 | Transfer RNA aminoacylation for protein translation | IARS, TARS, CARS, YARS, SARS, AARS, GARS, MARS | 11 | 1.53 × 10–03 |
GO:0032922 | Circadian regulation of gene expression | NAMPT, CRY2, NR1D1, RELB, PER2, PER1, BHLHE40, BHLHE41, PPARGC1A | 9 | 1.65 × 10–03 |
GO:0036498 | IRE1-mediated unfolded protein response | HYOU1, DNAJB9, TSPYL2, SYVN1, XBP1, PDIA5, SRPRB, DNAJC3, WIPI1 | 8 | 1.94 × 10–03 |
GO:0034599 | Cellular response to oxidative stress | DHRS2, PYCR1, XBP1, FOXO1, NFE2L2, RAD52, SESN2, PPARGC1A, ETV5 | 8 | 3.24 × 10–03 |
GO:0070059 | Intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress | CEBPB, XBP1, BBC3, CHAC1, TRIB3, PMAIP1, DDIT3 | 12 | 3.76 × 10–03 |
GO:1990440 | Positive regulation of transcription from RNA polymerase II promoter in response to endoplasmic reticulum stress | ATF3, CEBPB, XBP1, CREB3L1, DDIT3 | 23 | 6.52 × 10–03 |
GO:0007623 | Circadian rhythm | ATF5, NAMPT, CRY2, NR1D1, DBP, KLF10, PER2, PER1, PPARGC1A | 7 | 8.16 × 10–03 |
GO:0042149 | Cellular response to glucose starvation | TBL2, XBP1, ASNS, NFE2L2, PMAIP1, EIF2AK3 | 11 | 1.90 × 10–02 |
GO:0006355 | Regulation of transcription, DNA-templated | CREBRF, ZNF555, ZNF844, ZNF79, ING2, ZNF805, TAF1D, NR3C2, ZXDB, NR3C1, NFKB2, ZNF654, POLR2A, TSC22D3, TSPYL2, HEY1, ZNF709, SND1, ZNF442, BHLHE40, BHLHE41, INO80D, MYC, SAMD4A, ZBTB7B, ZNF563, BMP2, ZNF264, CEBPB, SNAPC1, RREB1, LRIF1, SMAD7, SPTY2D1, SIX4, PPARGC1A, ZBTB43, ABCG1, DDIT3, ATF5, IGSF1, CDKN2AIP, ZNF433, ZNF460, RBM39, SP8, NFIA, GOLGB1 | 2 | 1.83 × 10–02 |
GO:0042752 | Regulation of circadian rhythm | MAGEL2, CRY2, NR1D1, KLF10, PER2, PER1, PPARGC1A | 8 | 2.60 × 10–02 |
GO:0071456 | Cellular response to hypoxia | HYOU1, EIF4EBP1, TBL2, STC2, BBC3, VEGFA, NFE2L2, PMAIP1, PPARGC1A | 5 | 3.54 × 10–02 |
Eighteen biological functional ontologies are identified that are associated with these significant genes. Genes associated with these ontologies, fold enrichment, and corresponding adjusted P values are presented.
KEGG ID . | Pathway . | Genes in the Pathway . | Fold Enrichment . | Adjusted P Value . |
---|---|---|---|---|
GO:0030968 | Endoplasmic reticulum unfolded protein response | DERL2, CTH, TBL2, SYVN1, STC2, XBP1, AARS, CREB3L2, CREB3L1, YOD1, NFE2L2, EIF2AK3 | 15 | 6.28 × 10–07 |
GO:0034976 | Response to endoplasmic reticulum stress | CREBRF, HYOU1, HERPUD1, CEBPB, XBP1, BBC3, CREB3L2, TRIB3, PDIA5, FAM129A, EIF2AK3, DDIT3 | 9 | 9.34 × 10–05 |
GO:0000122 | Negative regulation of transcription from RNA polymerase II promoter | CREBRF, JDP2, NOG, CBX4, TRIB3, FOXO1, NFKB2, NFXL1, FLCN, TSC22D3, CRY2, NR1D1, HEY1, XBP1, SQSTM1, PER2, PER1, BHLHE40, SKIL, BHLHE41, MYC, KLF5, ERF, BMP2, RREB1, SMAD7, KLF10, RELB, NR4A3, MXD1, DDIT3, FNIP1, ATF3, TRPS1, VEGFA, NFIA | 3 | 7.08 × 10–05 |
GO:0045944 | Positive regulation of transcription from RNA polymerase II promoter | AKNA, NAMPT, NOG, FOXO1, NR3C1, NFKB2, FLCN, HEY1, XBP1, SQSTM1, NFAT5, CREB3L2, PER1, CREB3L1, FOSL1, MYC, ETV5, SERTAD1, KLF5, PID1, BMP2, CEBPB, MAFB, SMAD7, RELB, NR4A1, KLF15, SIX4, NR4A3, PPARGC1A, DDIT3, DDX58, ATF5, ATF3, DBP, CSRNP1, TRPS1, NCK1, VEGFA, IRF1, NFE2L2, NFIA, ACVR1 | 2 | 8.84 × 10–05 |
GO:0036499 | PERK-mediated unfolded protein response | HERPUD1, ATF3, ASNS, NFE2L2, EIF2AK3, DDIT3 | 27 | 4.93 × 10–04 |
GO:0006366 | Transcription from RNA polymerase II promoter | AKNA, NR3C1, NFKB2, NFXL1, POLR2A, XBP1, CREB3L2, NFAT5, CREB3L1, ETV5, MYC, FOSL1, KLF5, ZBTB7B, CEBPB, RREB1, MAFB, SIX4, KLF15, ATF5, ATF3, DBP, TRPS1, CSRNP1, IRF1, NFE2L2, NFIA | 3 | 7.66 × 10–04 |
GO:0006865 | Amino acid transport | SLC1A4, SLC1A5, SLC7A3, SLC7A1, SLC3A2, SLC7A5, MYC, SLC7A11 | 13 | 6.83 × 10–04 |
GO:0006418 | Transfer RNA aminoacylation for protein translation | IARS, TARS, CARS, YARS, SARS, AARS, GARS, MARS | 11 | 1.53 × 10–03 |
GO:0032922 | Circadian regulation of gene expression | NAMPT, CRY2, NR1D1, RELB, PER2, PER1, BHLHE40, BHLHE41, PPARGC1A | 9 | 1.65 × 10–03 |
GO:0036498 | IRE1-mediated unfolded protein response | HYOU1, DNAJB9, TSPYL2, SYVN1, XBP1, PDIA5, SRPRB, DNAJC3, WIPI1 | 8 | 1.94 × 10–03 |
GO:0034599 | Cellular response to oxidative stress | DHRS2, PYCR1, XBP1, FOXO1, NFE2L2, RAD52, SESN2, PPARGC1A, ETV5 | 8 | 3.24 × 10–03 |
GO:0070059 | Intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress | CEBPB, XBP1, BBC3, CHAC1, TRIB3, PMAIP1, DDIT3 | 12 | 3.76 × 10–03 |
GO:1990440 | Positive regulation of transcription from RNA polymerase II promoter in response to endoplasmic reticulum stress | ATF3, CEBPB, XBP1, CREB3L1, DDIT3 | 23 | 6.52 × 10–03 |
GO:0007623 | Circadian rhythm | ATF5, NAMPT, CRY2, NR1D1, DBP, KLF10, PER2, PER1, PPARGC1A | 7 | 8.16 × 10–03 |
GO:0042149 | Cellular response to glucose starvation | TBL2, XBP1, ASNS, NFE2L2, PMAIP1, EIF2AK3 | 11 | 1.90 × 10–02 |
GO:0006355 | Regulation of transcription, DNA-templated | CREBRF, ZNF555, ZNF844, ZNF79, ING2, ZNF805, TAF1D, NR3C2, ZXDB, NR3C1, NFKB2, ZNF654, POLR2A, TSC22D3, TSPYL2, HEY1, ZNF709, SND1, ZNF442, BHLHE40, BHLHE41, INO80D, MYC, SAMD4A, ZBTB7B, ZNF563, BMP2, ZNF264, CEBPB, SNAPC1, RREB1, LRIF1, SMAD7, SPTY2D1, SIX4, PPARGC1A, ZBTB43, ABCG1, DDIT3, ATF5, IGSF1, CDKN2AIP, ZNF433, ZNF460, RBM39, SP8, NFIA, GOLGB1 | 2 | 1.83 × 10–02 |
GO:0042752 | Regulation of circadian rhythm | MAGEL2, CRY2, NR1D1, KLF10, PER2, PER1, PPARGC1A | 8 | 2.60 × 10–02 |
GO:0071456 | Cellular response to hypoxia | HYOU1, EIF4EBP1, TBL2, STC2, BBC3, VEGFA, NFE2L2, PMAIP1, PPARGC1A | 5 | 3.54 × 10–02 |
KEGG ID . | Pathway . | Genes in the Pathway . | Fold Enrichment . | Adjusted P Value . |
---|---|---|---|---|
GO:0030968 | Endoplasmic reticulum unfolded protein response | DERL2, CTH, TBL2, SYVN1, STC2, XBP1, AARS, CREB3L2, CREB3L1, YOD1, NFE2L2, EIF2AK3 | 15 | 6.28 × 10–07 |
GO:0034976 | Response to endoplasmic reticulum stress | CREBRF, HYOU1, HERPUD1, CEBPB, XBP1, BBC3, CREB3L2, TRIB3, PDIA5, FAM129A, EIF2AK3, DDIT3 | 9 | 9.34 × 10–05 |
GO:0000122 | Negative regulation of transcription from RNA polymerase II promoter | CREBRF, JDP2, NOG, CBX4, TRIB3, FOXO1, NFKB2, NFXL1, FLCN, TSC22D3, CRY2, NR1D1, HEY1, XBP1, SQSTM1, PER2, PER1, BHLHE40, SKIL, BHLHE41, MYC, KLF5, ERF, BMP2, RREB1, SMAD7, KLF10, RELB, NR4A3, MXD1, DDIT3, FNIP1, ATF3, TRPS1, VEGFA, NFIA | 3 | 7.08 × 10–05 |
GO:0045944 | Positive regulation of transcription from RNA polymerase II promoter | AKNA, NAMPT, NOG, FOXO1, NR3C1, NFKB2, FLCN, HEY1, XBP1, SQSTM1, NFAT5, CREB3L2, PER1, CREB3L1, FOSL1, MYC, ETV5, SERTAD1, KLF5, PID1, BMP2, CEBPB, MAFB, SMAD7, RELB, NR4A1, KLF15, SIX4, NR4A3, PPARGC1A, DDIT3, DDX58, ATF5, ATF3, DBP, CSRNP1, TRPS1, NCK1, VEGFA, IRF1, NFE2L2, NFIA, ACVR1 | 2 | 8.84 × 10–05 |
GO:0036499 | PERK-mediated unfolded protein response | HERPUD1, ATF3, ASNS, NFE2L2, EIF2AK3, DDIT3 | 27 | 4.93 × 10–04 |
GO:0006366 | Transcription from RNA polymerase II promoter | AKNA, NR3C1, NFKB2, NFXL1, POLR2A, XBP1, CREB3L2, NFAT5, CREB3L1, ETV5, MYC, FOSL1, KLF5, ZBTB7B, CEBPB, RREB1, MAFB, SIX4, KLF15, ATF5, ATF3, DBP, TRPS1, CSRNP1, IRF1, NFE2L2, NFIA | 3 | 7.66 × 10–04 |
GO:0006865 | Amino acid transport | SLC1A4, SLC1A5, SLC7A3, SLC7A1, SLC3A2, SLC7A5, MYC, SLC7A11 | 13 | 6.83 × 10–04 |
GO:0006418 | Transfer RNA aminoacylation for protein translation | IARS, TARS, CARS, YARS, SARS, AARS, GARS, MARS | 11 | 1.53 × 10–03 |
GO:0032922 | Circadian regulation of gene expression | NAMPT, CRY2, NR1D1, RELB, PER2, PER1, BHLHE40, BHLHE41, PPARGC1A | 9 | 1.65 × 10–03 |
GO:0036498 | IRE1-mediated unfolded protein response | HYOU1, DNAJB9, TSPYL2, SYVN1, XBP1, PDIA5, SRPRB, DNAJC3, WIPI1 | 8 | 1.94 × 10–03 |
GO:0034599 | Cellular response to oxidative stress | DHRS2, PYCR1, XBP1, FOXO1, NFE2L2, RAD52, SESN2, PPARGC1A, ETV5 | 8 | 3.24 × 10–03 |
GO:0070059 | Intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress | CEBPB, XBP1, BBC3, CHAC1, TRIB3, PMAIP1, DDIT3 | 12 | 3.76 × 10–03 |
GO:1990440 | Positive regulation of transcription from RNA polymerase II promoter in response to endoplasmic reticulum stress | ATF3, CEBPB, XBP1, CREB3L1, DDIT3 | 23 | 6.52 × 10–03 |
GO:0007623 | Circadian rhythm | ATF5, NAMPT, CRY2, NR1D1, DBP, KLF10, PER2, PER1, PPARGC1A | 7 | 8.16 × 10–03 |
GO:0042149 | Cellular response to glucose starvation | TBL2, XBP1, ASNS, NFE2L2, PMAIP1, EIF2AK3 | 11 | 1.90 × 10–02 |
GO:0006355 | Regulation of transcription, DNA-templated | CREBRF, ZNF555, ZNF844, ZNF79, ING2, ZNF805, TAF1D, NR3C2, ZXDB, NR3C1, NFKB2, ZNF654, POLR2A, TSC22D3, TSPYL2, HEY1, ZNF709, SND1, ZNF442, BHLHE40, BHLHE41, INO80D, MYC, SAMD4A, ZBTB7B, ZNF563, BMP2, ZNF264, CEBPB, SNAPC1, RREB1, LRIF1, SMAD7, SPTY2D1, SIX4, PPARGC1A, ZBTB43, ABCG1, DDIT3, ATF5, IGSF1, CDKN2AIP, ZNF433, ZNF460, RBM39, SP8, NFIA, GOLGB1 | 2 | 1.83 × 10–02 |
GO:0042752 | Regulation of circadian rhythm | MAGEL2, CRY2, NR1D1, KLF10, PER2, PER1, PPARGC1A | 8 | 2.60 × 10–02 |
GO:0071456 | Cellular response to hypoxia | HYOU1, EIF4EBP1, TBL2, STC2, BBC3, VEGFA, NFE2L2, PMAIP1, PPARGC1A | 5 | 3.54 × 10–02 |
Eighteen biological functional ontologies are identified that are associated with these significant genes. Genes associated with these ontologies, fold enrichment, and corresponding adjusted P values are presented.
KEGG ID . | Pathway . | Genes in the Pathway . | Fold Enrichment . | Adjusted P Value . |
---|---|---|---|---|
GO:0006260 | DNA replication | CLSPN, TICRR, KIAA0101, POLA2, MCM10, CDC45, POLE2, ORC6, ORC1, CDC7, CDK1, GINS2, DTL, GINS3, GINS4, BRIP1, RMI2, MCM2, RBBP7, MCM3, RNASEH2A, MCM4, MCM5, BRCA1, CDK2, MCM6, POLD3, DNA2, RFC3, TIMELESS, POLD1, RRM2, RRM1, PCNA, CHAF1A, RBM14, CHAF1B, DSCC1 | 9 | 8.97 × 10–22 |
GO:0000082 | G1/S transition of mitotic cell cycle | CDC7, CDK1, IQGAP3, PKMYT1, POLA2, MCM2, RCC1, MCM10, MCM3, MCM4, CDK2, MCM5, MCM6, TYMS, CDC45, DHFR, POLE2, CDKN2C, RRM2, PCNA, ORC6, ORC1 | 8 | 2.22 × 10–10 |
GO:0051301 | Cell division | TRIOBP, KIFC1, CKS1B, STOX1, AURKA, RCC1, SPC24, NCAPH, CDCA7, OIP5, SKA3, TUBA1B, CCNO, HELLS, CDCA3, CDC7, CDK1, KIF11, PSRC1, TPX2, CENPF, KIF18B, BIRC5, CDC20, UBE2C, SMC2, MCM5, CDK2, SMC4, NCAPD2, CCNB1, FAM64A, CCNB2, TIMELESS, ZWINT, ANXA11, BUB1B, MIS18BP1 | 4 | 5.21 × 10–10 |
GO:0006270 | DNA replication initiation | CDC7, CDC45, POLE2, GINS4, ORC6, POLA2, MCM2, MCM3, MCM10, ORC1, MCM4, MCM5, MCM6 | 15 | 7.13 × 10–09 |
GO:0007067 | Mitotic nuclear division | TRIOBP, STOX1, PKMYT1, AURKA, ANLN, AURKB, RCC1, SPC24, OIP5, SKA3, HELLS, ASPM, CDCA3, CENPN, CDK1, KIF11, KIF15, TPX2, CENPF, BIRC5, CDC20, PBK, CDK2, FAM64A, CCNB2, TIMELESS, BUB1B, MIS18BP1 | 4 | 2.17 × 10–07 |
GO:0006271 | DNA strand elongation involved in DNA replication | POLD3, GINS1, GINS2, RFC3, POLD1, GINS3, GINS4, PCNA, POLA2 | 22 | 4.03 × 10–07 |
GO:0006281 | DNA repair | CLSPN, XRCC2, TICRR, FOXM1, FANCL, POLE2, DDX11, FANCI, RDM1, CDK1, NUDT1, RAD51AP1, GEN1, PIF1, BRCA1, CDK2, RAD51, UHRF1, NSMCE4A, FANCD2, POLD1, PARPBP, OGG1, CHAF1A, RBM14, CHAF1B | 4 | 1.10 × 10–06 |
GO:0032508 | DNA duplex unwinding | GINS1, GINS2, DNA2, CDC45, DDX11, PIF1, GINS4, BRIP1, MCM3, MCM5, DDX12P | 9 | 4.17 × 10–05 |
GO:0000731 | DNA synthesis involved in DNA repair | POLD3, DNA2, RFC3, XRCC2, RAD51AP1, POLD1, BRIP1, RMI2, BRCA1, RAD51 | 11 | 4.91 × 10–05 |
GO:0007062 | Sister chromatid cohesion | CENPO, CENPN, CENPM, CENPF, BIRC5, CDC20, AURKB, CENPK, CENPI, CENPH, SPC24, DDX11, CENPA, ZWINT, BUB1B | 5 | 1.09 × 10–04 |
GO:0034080 | CENP-A containing nucleosome assembly | CENPO, CENPN, CENPM, CENPA, OIP5, MIS18BP1, RBBP7, CENPK, CENPI, CENPH | 9 | 2.66 × 10–04 |
GO:0000722 | Telomere maintenance via recombination | POLD3, DNA2, RFC3, POLE2, POLD1, PCNA, POLA2, RAD51 | 9 | 2.66 × 10–03 |
GO:0000083 | Regulation of transcription involved in G1/S transition of mitotic cell cycle | CDK1, TYMS, CDC45, DHFR, RRM2, PCNA, ORC1 | 11 | 3.39 × 10–03 |
GO:0000070 | Mitotic sister chromatid segregation | KIFC1, CENPA, ZWINT, NUSAP1, KIF18B, ESPL1, SMC4 | 10 | 5.28 × 10–03 |
GO:0000732 | Strand displacement | DNA2, XRCC2, RAD51AP1, BRIP1, RMI2, BRCA1, RAD51 | 10 | 6.26 × 10–03 |
GO:0071897 | DNA biosynthetic process | CHRAC1, TYMS, CTGF, CENPF, POLA2, TK2, TK1 | 10 | 6.26 × 10–03 |
GO:0007049 | Cell cycle | E2F2, CKS1B, E2F3, DBF4B, GMNN, FOXM1, RBL1, AURKA, CDC20, AURKB, MCM2, DDX12P, BRCA1, UHRF1, ANXA11, CHAF1A, CHAF1B, CCNO | 3 | 8.56 × 10–03 |
GO:0000086 | G2/M transition of mitotic cell cycle | CCNB1, CDK1, NES, PLK4, CCNB2, FOXM1, KDM8, TPX2, PKMYT1, BIRC5, AURKA, CEP152, CDK2, TUBB4B | 4 | 8.29 × 10–03 |
GO:0006268 | DNA unwinding involved in DNA replication | MCM2, MCM4, TOP2A, RAD51, MCM6 | 19 | 9.47 × 10–03 |
GO:0000281 | Mitotic cytokinesis | CKAP2, KIF23, KIF4A, CENPA, NUSAP1, ANLN, KIF20A | 9 | 9.51 × 10–03 |
GO:0051726 | Regulation of cell cycle | E2F2, MAK, DTL, FOXM1, RBL1, PRR11, KIAA0101, CENPF, PKMYT1, MYBL2, CCNB1, SRSF5, CCNB2 | 4 | 1.09 × 10–02 |
GO:0008283 | Cell proliferation | KAT2A, CKS1B, CDK1, MKI67, DLGAP5, E2F8, KIF15, TPX2, CENPF, AURKB, RBBP7, MCM10, CSGALNACT1, TYMS, UHRF1, DNPH1, BOK, CKLF, CREG1, PCNA, BUB1B, TCF19, EMP2, IGFBP4 | 2 | 1.17 × 10–02 |
GO:0000727 | Double-strand break repair via break-induced replication | CDC7, GINS2, CDC45, GINS4 | 30 | 1.52 × 10–02 |
GO:0019985 | Translesion synthesis | POLD3, RFC3, DTL, POLD1, PCNA, KIAA0101, USP43 | 7 | 2.73 × 10–02 |
GO:0007076 | Mitotic chromosome condensation | NCAPH, NUSAP1, SMC2, SMC4, NCAPD2 | 12 | 4.09 × 10–02 |
KEGG ID . | Pathway . | Genes in the Pathway . | Fold Enrichment . | Adjusted P Value . |
---|---|---|---|---|
GO:0006260 | DNA replication | CLSPN, TICRR, KIAA0101, POLA2, MCM10, CDC45, POLE2, ORC6, ORC1, CDC7, CDK1, GINS2, DTL, GINS3, GINS4, BRIP1, RMI2, MCM2, RBBP7, MCM3, RNASEH2A, MCM4, MCM5, BRCA1, CDK2, MCM6, POLD3, DNA2, RFC3, TIMELESS, POLD1, RRM2, RRM1, PCNA, CHAF1A, RBM14, CHAF1B, DSCC1 | 9 | 8.97 × 10–22 |
GO:0000082 | G1/S transition of mitotic cell cycle | CDC7, CDK1, IQGAP3, PKMYT1, POLA2, MCM2, RCC1, MCM10, MCM3, MCM4, CDK2, MCM5, MCM6, TYMS, CDC45, DHFR, POLE2, CDKN2C, RRM2, PCNA, ORC6, ORC1 | 8 | 2.22 × 10–10 |
GO:0051301 | Cell division | TRIOBP, KIFC1, CKS1B, STOX1, AURKA, RCC1, SPC24, NCAPH, CDCA7, OIP5, SKA3, TUBA1B, CCNO, HELLS, CDCA3, CDC7, CDK1, KIF11, PSRC1, TPX2, CENPF, KIF18B, BIRC5, CDC20, UBE2C, SMC2, MCM5, CDK2, SMC4, NCAPD2, CCNB1, FAM64A, CCNB2, TIMELESS, ZWINT, ANXA11, BUB1B, MIS18BP1 | 4 | 5.21 × 10–10 |
GO:0006270 | DNA replication initiation | CDC7, CDC45, POLE2, GINS4, ORC6, POLA2, MCM2, MCM3, MCM10, ORC1, MCM4, MCM5, MCM6 | 15 | 7.13 × 10–09 |
GO:0007067 | Mitotic nuclear division | TRIOBP, STOX1, PKMYT1, AURKA, ANLN, AURKB, RCC1, SPC24, OIP5, SKA3, HELLS, ASPM, CDCA3, CENPN, CDK1, KIF11, KIF15, TPX2, CENPF, BIRC5, CDC20, PBK, CDK2, FAM64A, CCNB2, TIMELESS, BUB1B, MIS18BP1 | 4 | 2.17 × 10–07 |
GO:0006271 | DNA strand elongation involved in DNA replication | POLD3, GINS1, GINS2, RFC3, POLD1, GINS3, GINS4, PCNA, POLA2 | 22 | 4.03 × 10–07 |
GO:0006281 | DNA repair | CLSPN, XRCC2, TICRR, FOXM1, FANCL, POLE2, DDX11, FANCI, RDM1, CDK1, NUDT1, RAD51AP1, GEN1, PIF1, BRCA1, CDK2, RAD51, UHRF1, NSMCE4A, FANCD2, POLD1, PARPBP, OGG1, CHAF1A, RBM14, CHAF1B | 4 | 1.10 × 10–06 |
GO:0032508 | DNA duplex unwinding | GINS1, GINS2, DNA2, CDC45, DDX11, PIF1, GINS4, BRIP1, MCM3, MCM5, DDX12P | 9 | 4.17 × 10–05 |
GO:0000731 | DNA synthesis involved in DNA repair | POLD3, DNA2, RFC3, XRCC2, RAD51AP1, POLD1, BRIP1, RMI2, BRCA1, RAD51 | 11 | 4.91 × 10–05 |
GO:0007062 | Sister chromatid cohesion | CENPO, CENPN, CENPM, CENPF, BIRC5, CDC20, AURKB, CENPK, CENPI, CENPH, SPC24, DDX11, CENPA, ZWINT, BUB1B | 5 | 1.09 × 10–04 |
GO:0034080 | CENP-A containing nucleosome assembly | CENPO, CENPN, CENPM, CENPA, OIP5, MIS18BP1, RBBP7, CENPK, CENPI, CENPH | 9 | 2.66 × 10–04 |
GO:0000722 | Telomere maintenance via recombination | POLD3, DNA2, RFC3, POLE2, POLD1, PCNA, POLA2, RAD51 | 9 | 2.66 × 10–03 |
GO:0000083 | Regulation of transcription involved in G1/S transition of mitotic cell cycle | CDK1, TYMS, CDC45, DHFR, RRM2, PCNA, ORC1 | 11 | 3.39 × 10–03 |
GO:0000070 | Mitotic sister chromatid segregation | KIFC1, CENPA, ZWINT, NUSAP1, KIF18B, ESPL1, SMC4 | 10 | 5.28 × 10–03 |
GO:0000732 | Strand displacement | DNA2, XRCC2, RAD51AP1, BRIP1, RMI2, BRCA1, RAD51 | 10 | 6.26 × 10–03 |
GO:0071897 | DNA biosynthetic process | CHRAC1, TYMS, CTGF, CENPF, POLA2, TK2, TK1 | 10 | 6.26 × 10–03 |
GO:0007049 | Cell cycle | E2F2, CKS1B, E2F3, DBF4B, GMNN, FOXM1, RBL1, AURKA, CDC20, AURKB, MCM2, DDX12P, BRCA1, UHRF1, ANXA11, CHAF1A, CHAF1B, CCNO | 3 | 8.56 × 10–03 |
GO:0000086 | G2/M transition of mitotic cell cycle | CCNB1, CDK1, NES, PLK4, CCNB2, FOXM1, KDM8, TPX2, PKMYT1, BIRC5, AURKA, CEP152, CDK2, TUBB4B | 4 | 8.29 × 10–03 |
GO:0006268 | DNA unwinding involved in DNA replication | MCM2, MCM4, TOP2A, RAD51, MCM6 | 19 | 9.47 × 10–03 |
GO:0000281 | Mitotic cytokinesis | CKAP2, KIF23, KIF4A, CENPA, NUSAP1, ANLN, KIF20A | 9 | 9.51 × 10–03 |
GO:0051726 | Regulation of cell cycle | E2F2, MAK, DTL, FOXM1, RBL1, PRR11, KIAA0101, CENPF, PKMYT1, MYBL2, CCNB1, SRSF5, CCNB2 | 4 | 1.09 × 10–02 |
GO:0008283 | Cell proliferation | KAT2A, CKS1B, CDK1, MKI67, DLGAP5, E2F8, KIF15, TPX2, CENPF, AURKB, RBBP7, MCM10, CSGALNACT1, TYMS, UHRF1, DNPH1, BOK, CKLF, CREG1, PCNA, BUB1B, TCF19, EMP2, IGFBP4 | 2 | 1.17 × 10–02 |
GO:0000727 | Double-strand break repair via break-induced replication | CDC7, GINS2, CDC45, GINS4 | 30 | 1.52 × 10–02 |
GO:0019985 | Translesion synthesis | POLD3, RFC3, DTL, POLD1, PCNA, KIAA0101, USP43 | 7 | 2.73 × 10–02 |
GO:0007076 | Mitotic chromosome condensation | NCAPH, NUSAP1, SMC2, SMC4, NCAPD2 | 12 | 4.09 × 10–02 |
Twenty-five biological functional ontologies are identified that are associated with these significant genes. Genes associated with these ontologies, fold enrichment, and corresponding adjusted P values are presented.
KEGG ID . | Pathway . | Genes in the Pathway . | Fold Enrichment . | Adjusted P Value . |
---|---|---|---|---|
GO:0006260 | DNA replication | CLSPN, TICRR, KIAA0101, POLA2, MCM10, CDC45, POLE2, ORC6, ORC1, CDC7, CDK1, GINS2, DTL, GINS3, GINS4, BRIP1, RMI2, MCM2, RBBP7, MCM3, RNASEH2A, MCM4, MCM5, BRCA1, CDK2, MCM6, POLD3, DNA2, RFC3, TIMELESS, POLD1, RRM2, RRM1, PCNA, CHAF1A, RBM14, CHAF1B, DSCC1 | 9 | 8.97 × 10–22 |
GO:0000082 | G1/S transition of mitotic cell cycle | CDC7, CDK1, IQGAP3, PKMYT1, POLA2, MCM2, RCC1, MCM10, MCM3, MCM4, CDK2, MCM5, MCM6, TYMS, CDC45, DHFR, POLE2, CDKN2C, RRM2, PCNA, ORC6, ORC1 | 8 | 2.22 × 10–10 |
GO:0051301 | Cell division | TRIOBP, KIFC1, CKS1B, STOX1, AURKA, RCC1, SPC24, NCAPH, CDCA7, OIP5, SKA3, TUBA1B, CCNO, HELLS, CDCA3, CDC7, CDK1, KIF11, PSRC1, TPX2, CENPF, KIF18B, BIRC5, CDC20, UBE2C, SMC2, MCM5, CDK2, SMC4, NCAPD2, CCNB1, FAM64A, CCNB2, TIMELESS, ZWINT, ANXA11, BUB1B, MIS18BP1 | 4 | 5.21 × 10–10 |
GO:0006270 | DNA replication initiation | CDC7, CDC45, POLE2, GINS4, ORC6, POLA2, MCM2, MCM3, MCM10, ORC1, MCM4, MCM5, MCM6 | 15 | 7.13 × 10–09 |
GO:0007067 | Mitotic nuclear division | TRIOBP, STOX1, PKMYT1, AURKA, ANLN, AURKB, RCC1, SPC24, OIP5, SKA3, HELLS, ASPM, CDCA3, CENPN, CDK1, KIF11, KIF15, TPX2, CENPF, BIRC5, CDC20, PBK, CDK2, FAM64A, CCNB2, TIMELESS, BUB1B, MIS18BP1 | 4 | 2.17 × 10–07 |
GO:0006271 | DNA strand elongation involved in DNA replication | POLD3, GINS1, GINS2, RFC3, POLD1, GINS3, GINS4, PCNA, POLA2 | 22 | 4.03 × 10–07 |
GO:0006281 | DNA repair | CLSPN, XRCC2, TICRR, FOXM1, FANCL, POLE2, DDX11, FANCI, RDM1, CDK1, NUDT1, RAD51AP1, GEN1, PIF1, BRCA1, CDK2, RAD51, UHRF1, NSMCE4A, FANCD2, POLD1, PARPBP, OGG1, CHAF1A, RBM14, CHAF1B | 4 | 1.10 × 10–06 |
GO:0032508 | DNA duplex unwinding | GINS1, GINS2, DNA2, CDC45, DDX11, PIF1, GINS4, BRIP1, MCM3, MCM5, DDX12P | 9 | 4.17 × 10–05 |
GO:0000731 | DNA synthesis involved in DNA repair | POLD3, DNA2, RFC3, XRCC2, RAD51AP1, POLD1, BRIP1, RMI2, BRCA1, RAD51 | 11 | 4.91 × 10–05 |
GO:0007062 | Sister chromatid cohesion | CENPO, CENPN, CENPM, CENPF, BIRC5, CDC20, AURKB, CENPK, CENPI, CENPH, SPC24, DDX11, CENPA, ZWINT, BUB1B | 5 | 1.09 × 10–04 |
GO:0034080 | CENP-A containing nucleosome assembly | CENPO, CENPN, CENPM, CENPA, OIP5, MIS18BP1, RBBP7, CENPK, CENPI, CENPH | 9 | 2.66 × 10–04 |
GO:0000722 | Telomere maintenance via recombination | POLD3, DNA2, RFC3, POLE2, POLD1, PCNA, POLA2, RAD51 | 9 | 2.66 × 10–03 |
GO:0000083 | Regulation of transcription involved in G1/S transition of mitotic cell cycle | CDK1, TYMS, CDC45, DHFR, RRM2, PCNA, ORC1 | 11 | 3.39 × 10–03 |
GO:0000070 | Mitotic sister chromatid segregation | KIFC1, CENPA, ZWINT, NUSAP1, KIF18B, ESPL1, SMC4 | 10 | 5.28 × 10–03 |
GO:0000732 | Strand displacement | DNA2, XRCC2, RAD51AP1, BRIP1, RMI2, BRCA1, RAD51 | 10 | 6.26 × 10–03 |
GO:0071897 | DNA biosynthetic process | CHRAC1, TYMS, CTGF, CENPF, POLA2, TK2, TK1 | 10 | 6.26 × 10–03 |
GO:0007049 | Cell cycle | E2F2, CKS1B, E2F3, DBF4B, GMNN, FOXM1, RBL1, AURKA, CDC20, AURKB, MCM2, DDX12P, BRCA1, UHRF1, ANXA11, CHAF1A, CHAF1B, CCNO | 3 | 8.56 × 10–03 |
GO:0000086 | G2/M transition of mitotic cell cycle | CCNB1, CDK1, NES, PLK4, CCNB2, FOXM1, KDM8, TPX2, PKMYT1, BIRC5, AURKA, CEP152, CDK2, TUBB4B | 4 | 8.29 × 10–03 |
GO:0006268 | DNA unwinding involved in DNA replication | MCM2, MCM4, TOP2A, RAD51, MCM6 | 19 | 9.47 × 10–03 |
GO:0000281 | Mitotic cytokinesis | CKAP2, KIF23, KIF4A, CENPA, NUSAP1, ANLN, KIF20A | 9 | 9.51 × 10–03 |
GO:0051726 | Regulation of cell cycle | E2F2, MAK, DTL, FOXM1, RBL1, PRR11, KIAA0101, CENPF, PKMYT1, MYBL2, CCNB1, SRSF5, CCNB2 | 4 | 1.09 × 10–02 |
GO:0008283 | Cell proliferation | KAT2A, CKS1B, CDK1, MKI67, DLGAP5, E2F8, KIF15, TPX2, CENPF, AURKB, RBBP7, MCM10, CSGALNACT1, TYMS, UHRF1, DNPH1, BOK, CKLF, CREG1, PCNA, BUB1B, TCF19, EMP2, IGFBP4 | 2 | 1.17 × 10–02 |
GO:0000727 | Double-strand break repair via break-induced replication | CDC7, GINS2, CDC45, GINS4 | 30 | 1.52 × 10–02 |
GO:0019985 | Translesion synthesis | POLD3, RFC3, DTL, POLD1, PCNA, KIAA0101, USP43 | 7 | 2.73 × 10–02 |
GO:0007076 | Mitotic chromosome condensation | NCAPH, NUSAP1, SMC2, SMC4, NCAPD2 | 12 | 4.09 × 10–02 |
KEGG ID . | Pathway . | Genes in the Pathway . | Fold Enrichment . | Adjusted P Value . |
---|---|---|---|---|
GO:0006260 | DNA replication | CLSPN, TICRR, KIAA0101, POLA2, MCM10, CDC45, POLE2, ORC6, ORC1, CDC7, CDK1, GINS2, DTL, GINS3, GINS4, BRIP1, RMI2, MCM2, RBBP7, MCM3, RNASEH2A, MCM4, MCM5, BRCA1, CDK2, MCM6, POLD3, DNA2, RFC3, TIMELESS, POLD1, RRM2, RRM1, PCNA, CHAF1A, RBM14, CHAF1B, DSCC1 | 9 | 8.97 × 10–22 |
GO:0000082 | G1/S transition of mitotic cell cycle | CDC7, CDK1, IQGAP3, PKMYT1, POLA2, MCM2, RCC1, MCM10, MCM3, MCM4, CDK2, MCM5, MCM6, TYMS, CDC45, DHFR, POLE2, CDKN2C, RRM2, PCNA, ORC6, ORC1 | 8 | 2.22 × 10–10 |
GO:0051301 | Cell division | TRIOBP, KIFC1, CKS1B, STOX1, AURKA, RCC1, SPC24, NCAPH, CDCA7, OIP5, SKA3, TUBA1B, CCNO, HELLS, CDCA3, CDC7, CDK1, KIF11, PSRC1, TPX2, CENPF, KIF18B, BIRC5, CDC20, UBE2C, SMC2, MCM5, CDK2, SMC4, NCAPD2, CCNB1, FAM64A, CCNB2, TIMELESS, ZWINT, ANXA11, BUB1B, MIS18BP1 | 4 | 5.21 × 10–10 |
GO:0006270 | DNA replication initiation | CDC7, CDC45, POLE2, GINS4, ORC6, POLA2, MCM2, MCM3, MCM10, ORC1, MCM4, MCM5, MCM6 | 15 | 7.13 × 10–09 |
GO:0007067 | Mitotic nuclear division | TRIOBP, STOX1, PKMYT1, AURKA, ANLN, AURKB, RCC1, SPC24, OIP5, SKA3, HELLS, ASPM, CDCA3, CENPN, CDK1, KIF11, KIF15, TPX2, CENPF, BIRC5, CDC20, PBK, CDK2, FAM64A, CCNB2, TIMELESS, BUB1B, MIS18BP1 | 4 | 2.17 × 10–07 |
GO:0006271 | DNA strand elongation involved in DNA replication | POLD3, GINS1, GINS2, RFC3, POLD1, GINS3, GINS4, PCNA, POLA2 | 22 | 4.03 × 10–07 |
GO:0006281 | DNA repair | CLSPN, XRCC2, TICRR, FOXM1, FANCL, POLE2, DDX11, FANCI, RDM1, CDK1, NUDT1, RAD51AP1, GEN1, PIF1, BRCA1, CDK2, RAD51, UHRF1, NSMCE4A, FANCD2, POLD1, PARPBP, OGG1, CHAF1A, RBM14, CHAF1B | 4 | 1.10 × 10–06 |
GO:0032508 | DNA duplex unwinding | GINS1, GINS2, DNA2, CDC45, DDX11, PIF1, GINS4, BRIP1, MCM3, MCM5, DDX12P | 9 | 4.17 × 10–05 |
GO:0000731 | DNA synthesis involved in DNA repair | POLD3, DNA2, RFC3, XRCC2, RAD51AP1, POLD1, BRIP1, RMI2, BRCA1, RAD51 | 11 | 4.91 × 10–05 |
GO:0007062 | Sister chromatid cohesion | CENPO, CENPN, CENPM, CENPF, BIRC5, CDC20, AURKB, CENPK, CENPI, CENPH, SPC24, DDX11, CENPA, ZWINT, BUB1B | 5 | 1.09 × 10–04 |
GO:0034080 | CENP-A containing nucleosome assembly | CENPO, CENPN, CENPM, CENPA, OIP5, MIS18BP1, RBBP7, CENPK, CENPI, CENPH | 9 | 2.66 × 10–04 |
GO:0000722 | Telomere maintenance via recombination | POLD3, DNA2, RFC3, POLE2, POLD1, PCNA, POLA2, RAD51 | 9 | 2.66 × 10–03 |
GO:0000083 | Regulation of transcription involved in G1/S transition of mitotic cell cycle | CDK1, TYMS, CDC45, DHFR, RRM2, PCNA, ORC1 | 11 | 3.39 × 10–03 |
GO:0000070 | Mitotic sister chromatid segregation | KIFC1, CENPA, ZWINT, NUSAP1, KIF18B, ESPL1, SMC4 | 10 | 5.28 × 10–03 |
GO:0000732 | Strand displacement | DNA2, XRCC2, RAD51AP1, BRIP1, RMI2, BRCA1, RAD51 | 10 | 6.26 × 10–03 |
GO:0071897 | DNA biosynthetic process | CHRAC1, TYMS, CTGF, CENPF, POLA2, TK2, TK1 | 10 | 6.26 × 10–03 |
GO:0007049 | Cell cycle | E2F2, CKS1B, E2F3, DBF4B, GMNN, FOXM1, RBL1, AURKA, CDC20, AURKB, MCM2, DDX12P, BRCA1, UHRF1, ANXA11, CHAF1A, CHAF1B, CCNO | 3 | 8.56 × 10–03 |
GO:0000086 | G2/M transition of mitotic cell cycle | CCNB1, CDK1, NES, PLK4, CCNB2, FOXM1, KDM8, TPX2, PKMYT1, BIRC5, AURKA, CEP152, CDK2, TUBB4B | 4 | 8.29 × 10–03 |
GO:0006268 | DNA unwinding involved in DNA replication | MCM2, MCM4, TOP2A, RAD51, MCM6 | 19 | 9.47 × 10–03 |
GO:0000281 | Mitotic cytokinesis | CKAP2, KIF23, KIF4A, CENPA, NUSAP1, ANLN, KIF20A | 9 | 9.51 × 10–03 |
GO:0051726 | Regulation of cell cycle | E2F2, MAK, DTL, FOXM1, RBL1, PRR11, KIAA0101, CENPF, PKMYT1, MYBL2, CCNB1, SRSF5, CCNB2 | 4 | 1.09 × 10–02 |
GO:0008283 | Cell proliferation | KAT2A, CKS1B, CDK1, MKI67, DLGAP5, E2F8, KIF15, TPX2, CENPF, AURKB, RBBP7, MCM10, CSGALNACT1, TYMS, UHRF1, DNPH1, BOK, CKLF, CREG1, PCNA, BUB1B, TCF19, EMP2, IGFBP4 | 2 | 1.17 × 10–02 |
GO:0000727 | Double-strand break repair via break-induced replication | CDC7, GINS2, CDC45, GINS4 | 30 | 1.52 × 10–02 |
GO:0019985 | Translesion synthesis | POLD3, RFC3, DTL, POLD1, PCNA, KIAA0101, USP43 | 7 | 2.73 × 10–02 |
GO:0007076 | Mitotic chromosome condensation | NCAPH, NUSAP1, SMC2, SMC4, NCAPD2 | 12 | 4.09 × 10–02 |
Twenty-five biological functional ontologies are identified that are associated with these significant genes. Genes associated with these ontologies, fold enrichment, and corresponding adjusted P values are presented.
DISCUSSION
We have analyzed multirelational infectome, diseasome, and disease comorbidity relationships of ZIKV infections with other diseases or infections based on their genetic associations. Based on the combined genetics data, our disease networks disclosed potentially novel disease relationships not captured by previous individual studies. Our results indicate that ZIKV-induced transcriptomic alterations are associated with established clinical pathologies and such a combination of molecular data could help to build novel hypotheses about disease mechanisms.
ZIKV infection has a possibility of long-term neurological effects in adults following infection of neural progenitors [27]. The suspected association between ZIKV and neurological complications such as GBS and congenital malformation needs to be confirmed to control and reduce the negative impacts of the infection. GBS is an acute, immune-mediated polyradiculoneuropathy typically occurring after minor viral and bacterial infections. The risk of GBS increases with age, with men more commonly affected than women. The results of our study suggest that ZIKV should be added to the list of infectious pathogens susceptible to cause GBS.
Our phylogenetic comparison of ZIKV with similar viruses can greatly improve our understanding of the relationships among the Flaviviridae. We have also studied the differences between ZIKV and other similar infections in the host response. This enables rapid assessment of viral properties and the ability to anticipate possible differences in human clinical responses to ZIKV and other infections and their impact on comorbidities with respect to general comorbid conditions [28]. From the phylogenetic and infectome–diseasome analysis, we identified that ZIKA virus infection most resembles dengue virus infection.
We identified significant genes that offer a potential to verify susceptibility/risk factor assessment for the ZIKV infection. Beyond the identification of individual genes, our analysis also focused on the identification and characterization of biological functions associated with these genes. Moreover, disease genes play a central role in the human interactomes through the pathways. We found 3 important pathways (protein processing in endoplasmic reticulum, aminoacyl-tRNA biosynthesis, and circadian rhythm) associated with the ZIKV-infected up-regulated genes and 5 important pathways (cell cycle, DNA replication, HTLV-I (Human T-lymphotropic virus) infection, pyrimidine metabolism, and Fanconi anemia pathway) associated with the ZIKV-infected down-regulated genes. We showed that ZIKV down-regulates a network of targets related to DNA replication and molecules associated with cell cycle progression. The cell cycle and DNA replication pathways are also a major signaling pathway that regulates inflammation and mediates the responses of target cells to inflammatory cytokines. Activation of these pathways plays a central role in inflammation through regulation of proinflammatory cytokines, chemokines, and inducible enzymes such as cyclooxygenase-2 and inducible nitric oxide synthase. Moreover, it has recently been reported that ZIKV-infected fetal brains display a number of inflammatory markers, including diffuse astrogliosis, and activation of macrophages and microglia [27]. These results point to biological mechanisms implicated in brain malformations, which are important for understanding of ZIKV infection and possibly exploited as therapeutic potential targets to mitigate it. Furthermore, ZIKV infection triggers responses by the innate immune system, and modulating this response should be considered in the search for therapeutic interventions against Zika.
In summary, our results fill a major gap in our knowledge about ZIKV biology and serve as an entry point to establish a mechanistic link between ZIKV and other similar infections and diseases. Our approach could be used for further studies of the pathogenesis of ZIKV infections. However, more studies are needed to improve our understanding of ZIKV disease, including the potential epidemiologic and immunopathogenesis interactions between ZIKV and other comorbidities. This study provides not only networks between the genes for understanding the pathogenic properties of ZIKV but also maps significant pathways for the future development of novel therapeutic strategies. We may use this information to predict potential effective drugs against ZIKV, a method that could be more generally used to identify candidate therapeutics in future disease outbreaks.
CONCLUSIONS
In this study, we have considered genomic data to quantify the ZIKV infection–centered infectome, diseasome, and comorbidity associations. Detecting comorbidity in a large population is of clinical interest as it may reveal new information useful for cause of diseases as well as for new treatment strategies. This study demonstrates the value of an integrated approach in revealing disease relationships and new opportunities for therapeutic applications. This kind of approach will be helpful for making genomic evidence–based recommendations about accurate disease identification and the prediction of disease comorbidities. Thus, genomic information–based personalized medicine will give fundamental new insights into disease mechanisms, and hence will open new opportunities for diagnosis, therapy, and prevention from the disease comorbidities.
Supplementary Data
Supplementary materials are available at The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.
Notes
Acknowledgments. The authors thank the 2 anonymous reviewers for their comments and suggestions.
Financial support. This work was supported by the European Union Mimomics grant FP7-Health-F5-2012 under grant agreement number 305280.
Potential conflicts of interest. Both authors: No reported conflicts of interest. Both authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
References